Advanced Reinforcement Learning in Python- cutting-edge DQNs

Advanced Reinforcement Learning in Python: cutting-edge DQNs, available at $69.99, has an average rating of 4.85, with 104 lectures, based on 81 reviews, and has 1430 subscribers.

You will learn about Master some of the most advanced Reinforcement Learning algorithms. Learn how to create AIs that can act in a complex environment to achieve their goals. Create from scratch advanced Reinforcement Learning agents using Pythons most popular tools (PyTorch Lightning, OpenAI gym, Optuna) Learn how to perform hyperparameter tuning (Choosing the best experimental conditions for our AI to learn) Fundamentally understand the learning process for each algorithm. Debug and extend the algorithms presented. Understand and implement new algorithms from research papers. This course is ideal for individuals who are Developers who want to get a job in Machine Learning. or Data scientists/analysts and ML practitioners seeking to expand their breadth of knowledge. or Robotics students and researchers. or Engineering students and researchers. It is particularly useful for Developers who want to get a job in Machine Learning. or Data scientists/analysts and ML practitioners seeking to expand their breadth of knowledge. or Robotics students and researchers. or Engineering students and researchers.

Enroll now: Advanced Reinforcement Learning in Python: cutting-edge DQNs

Summary

Title: Advanced Reinforcement Learning in Python: cutting-edge DQNs

Price: $69.99

Average Rating: 4.85

Number of Lectures: 104

Number of Published Lectures: 103

Number of Curriculum Items: 104

Number of Published Curriculum Objects: 103

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Master some of the most advanced Reinforcement Learning algorithms.

Learn how to create AIs that can act in a complex environment to achieve their goals.

Create from scratch advanced Reinforcement Learning agents using Pythons most popular tools (PyTorch Lightning, OpenAI gym, Optuna)

Learn how to perform hyperparameter tuning (Choosing the best experimental conditions for our AI to learn)

Fundamentally understand the learning process for each algorithm.

Debug and extend the algorithms presented.

Understand and implement new algorithms from research papers.

Who Should Attend

Developers who want to get a job in Machine Learning.

Data scientists/analysts and ML practitioners seeking to expand their breadth of knowledge.

Robotics students and researchers.

Engineering students and researchers.

Target Audiences

Developers who want to get a job in Machine Learning.

Data scientists/analysts and ML practitioners seeking to expand their breadth of knowledge.

Robotics students and researchers.

Engineering students and researchers.

This is the most complete Advanced Reinforcement Learning course on Udemy. In it, you will learn to implement some of the most powerful Deep Reinforcement Learning algorithms in Python using PyTorch and PyTorch lightning. You will implement from scratch adaptive algorithms that solve control tasks based on experience. You will learn to combine these techniques with Neural Networks and Deep Learning methods to create adaptive Artificial Intelligence agents capable of solving decision-making tasks.

This course will introduce you to the state of the art in Reinforcement Learning techniques. It will also prepare you for the next courses in this series, where we will explore other advanced methods that excel in other types of task.

The course is focused on developing practical skills. Therefore, after learning the most important concepts of each family of methods, we will implement one or more of their algorithms in jupyter notebooks, from scratch.

Leveling modules: 

– Refresher: The Markov decision process (MDP).

– Refresher: Q-Learning.

– Refresher: Brief introduction to Neural Networks.

– Refresher: Deep Q-Learning.

Advanced Reinforcement Learning:

– PyTorch Lightning.

– Hyperparameter tuning with Optuna.

– Reinforcement Learning with image inputs

– Double Deep Q-Learning

– Dueling Deep Q-Networks

– Prioritized Experience Replay (PER)

– Distributional Deep Q-Networks

– Noisy Deep Q-Networks

– N-step Deep Q-Learning

– Rainbow Deep Q-Learning

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Reinforcement Learning series

Lecture 3: Google Colab

Lecture 4: Where to begin

Lecture 5: Complete code

Lecture 6: Connect with me on social media

Chapter 2: Refresher: The Markov Decision Process (MDP)

Lecture 1: Module overview

Lecture 2: Elements common to all control tasks

Lecture 3: The Markov decision process (MDP)

Lecture 4: Types of Markov decision process

Lecture 5: Trajectory vs episode

Lecture 6: Reward vs Return

Lecture 7: Discount factor

Lecture 8: Policy

Lecture 9: State values v(s) and action values q(s,a)

Lecture 10: Bellman equations

Lecture 11: Solving a Markov decision process

Chapter 3: Refresher: Q-Learning

Lecture 1: Module overview

Lecture 2: Temporal difference methods

Lecture 3: Solving control tasks with temporal difference method

Lecture 4: Q-Learning

Lecture 5: Advantages of temporal difference methods

Chapter 4: Refresher: Brief introduction to Neural Networks

Lecture 1: Module overview

Lecture 2: Function approximators

Lecture 3: Artificial Neural Networks

Lecture 4: Artificial Neurons

Lecture 5: How to represent a Neural Network

Lecture 6: Stochastic Gradient Descent

Lecture 7: Neural Network optimization

Chapter 5: Refresher: Deep Q-Learning

Lecture 1: Module overview

Lecture 2: Deep Q-Learning

Lecture 3: Experience replay

Lecture 4: Target Network

Chapter 6: PyTorch Lightning

Lecture 1: PyTorch Lightning

Lecture 2: Link to the code notebook

Lecture 3: Introduction to PyTorch Lightning

Lecture 4: Create the Deep Q-Network

Lecture 5: Create the policy

Lecture 6: Create the replay buffer

Lecture 7: Create the environment

Lecture 8: Define the class for the Deep Q-Learning algorithm

Lecture 9: Define the play_episode() function

Lecture 10: Prepare the data loader and the optimizer

Lecture 11: Define the train_step() method

Lecture 12: Define the train_epoch_end() method

Lecture 13: [Important] Lecture correction

Lecture 14: Train the Deep Q-Learning algorithm

Lecture 15: Explore the resulting agent

Chapter 7: Hyperparameter tuning with Optuna

Lecture 1: Hyperparameter tuning with Optuna

Lecture 2: Link to the code notebook

Lecture 3: Log average return

Lecture 4: Define the objective function

Lecture 5: Create and launch the hyperparameter tuning job

Lecture 6: Explore the best trial

Chapter 8: Double Deep Q-Learning

Lecture 1: Maximization bias and Double Deep Q-Learning

Lecture 2: Link to the code notebook

Lecture 3: Create the Double Deep Q-Learning algorithm

Lecture 4: Check the resulting agent

Chapter 9: Dueling Deep Q-Networks

Lecture 1: Dueling Deep Q-Networks

Lecture 2: Link to the code notebook

Lecture 3: Create the dueling DQN

Lecture 4: Observation and reward normalization

Lecture 5: Create the environment – Part 1

Lecture 6: Create the environment – Part 2

Lecture 7: Implement Deep Q-Learning

Lecture 8: Check the resulting agent

Chapter 10: Prioritized Experience Replay

Lecture 1: Prioritized Experience Replay

Lecture 2: Link to the code notebook

Lecture 3: DQN for visual inputs

Lecture 4: Prioritized Experience Repay Buffer

Lecture 5: Create the environment

Lecture 6: Implement the Deep Q-Learning algorithm with Prioritized Experience Replay

Lecture 7: Errata Lecture

Lecture 8: Launch the training process

Lecture 9: Check the resulting agent

Chapter 11: Noisy Deep Q-Networks

Lecture 1: Noisy Deep Q-Networks

Lecture 2: Link to the code notebook

Lecture 3: Create the noisy linear layer class

Lecture 4: Create the Deep Q-Network

Lecture 5: Create the policy

Lecture 6: Create the environment

Lecture 7: Train the algorithm

Lecture 8: Check the results

Chapter 12: N-step Deep Q-Learning

Lecture 1: N-step Deep Q-Learning

Lecture 2: Link to the code notebook

Lecture 3: N-step Deep Q-Learning – Part 1

Lecture 4: N-step Deep Q-Learning – Part 2

Lecture 5: N-step Deep Q-Learning – Part 3

Instructors

Escape Velocity Labs
Hands-on, comprehensive AI courses

Rating Distribution

1 stars: 2 votes

2 stars: 1 votes

3 stars: 6 votes

4 stars: 9 votes

5 stars: 63 votes

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